Overview

Dataset statistics

Number of variables19
Number of observations31304
Missing cells69772
Missing cells (%)11.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.5 MiB
Average record size in memory152.0 B

Variable types

Categorical9
Numeric10

Alerts

Bene_Geo_Desc has a high cardinality: 56 distinct values High cardinality
Total_Bene_Hosp is highly correlated with Total_Mth_EnrlHigh correlation
Total_Mth_Enrl is highly correlated with Total_Bene_HospHigh correlation
Total_Bene_Enr_Hosp_Per100K is highly correlated with MonthHigh correlation
Pct_Dschrg_SNF is highly correlated with Pct_Dschrg_HomeHigh correlation
Pct_Dschrg_Home is highly correlated with Bene_Geo_Desc and 3 other fieldsHigh correlation
Pct_Dschrg_Hspc is highly correlated with Bene_Age_Desc and 1 other fieldsHigh correlation
Month is highly correlated with Total_Bene_Enr_Hosp_Per100KHigh correlation
Bene_Geo_Desc is highly correlated with Pct_Dschrg_Home and 2 other fieldsHigh correlation
Bene_Age_Desc is highly correlated with Pct_Dschrg_Home and 1 other fieldsHigh correlation
Pct_Dschrg_HomeHealth is highly correlated with Bene_Geo_DescHigh correlation
Pct_Dschrg_Other is highly correlated with Bene_Geo_DescHigh correlation
Total_Bene_Hosp has 6919 (22.1%) missing values Missing
Total_Bene_Enr_Hosp_Per100K has 6919 (22.1%) missing values Missing
AVG_los has 7987 (25.5%) missing values Missing
Pct_Dschrg_SNF has 7987 (25.5%) missing values Missing
Pct_Dschrg_Expired has 7987 (25.5%) missing values Missing
Pct_Dschrg_Home has 7987 (25.5%) missing values Missing
Pct_Dschrg_Hspc has 7987 (25.5%) missing values Missing
Pct_Dschrg_HomeHealth has 7987 (25.5%) missing values Missing
Pct_Dschrg_Other has 7987 (25.5%) missing values Missing
Total_Bene_Hosp is highly skewed (γ1 = 21.34513137) Skewed
Year is uniformly distributed Uniform
Month is uniformly distributed Uniform
Total_Bene_Hosp has 1069 (3.4%) zeros Zeros
Total_Bene_Enr_Hosp_Per100K has 1069 (3.4%) zeros Zeros
Pct_Dschrg_SNF has 429 (1.4%) zeros Zeros
Pct_Dschrg_Expired has 411 (1.3%) zeros Zeros
Pct_Dschrg_Hspc has 2647 (8.5%) zeros Zeros
Pct_Dschrg_Other has 1028 (3.3%) zeros Zeros

Reproduction

Analysis started2022-10-27 04:46:00.033126
Analysis finished2022-10-27 04:46:31.113211
Duration31.08 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

Year
Categorical

UNIFORM

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
2020
15652 
2021
15652 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters125216
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
202015652
50.0%
202115652
50.0%

Length

2022-10-27T00:46:31.224939image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:31.381354image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
202015652
50.0%
202115652
50.0%

Most occurring characters

ValueCountFrequency (%)
262608
50.0%
046956
37.5%
115652
 
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number125216
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
262608
50.0%
046956
37.5%
115652
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common125216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
262608
50.0%
046956
37.5%
115652
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII125216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
262608
50.0%
046956
37.5%
115652
 
12.5%

Month
Categorical

HIGH CORRELATION
UNIFORM

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
11
2408 
12
2408 
4
2408 
7
2408 
8
2408 
Other values (8)
19264 

Length

Max length7
Median length1
Mean length1.692307692
Min length1

Characters and Unicode

Total characters52976
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverall
2nd rowOverall
3rd rowOverall
4th rowOverall
5th rowOverall

Common Values

ValueCountFrequency (%)
112408
 
7.7%
122408
 
7.7%
42408
 
7.7%
72408
 
7.7%
82408
 
7.7%
102408
 
7.7%
52408
 
7.7%
Overall2408
 
7.7%
12408
 
7.7%
62408
 
7.7%
Other values (3)7224
23.1%

Length

2022-10-27T00:46:31.521013image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
112408
 
7.7%
122408
 
7.7%
42408
 
7.7%
72408
 
7.7%
82408
 
7.7%
102408
 
7.7%
52408
 
7.7%
overall2408
 
7.7%
12408
 
7.7%
62408
 
7.7%
Other values (3)7224
23.1%

Most occurring characters

ValueCountFrequency (%)
112040
22.7%
24816
 
9.1%
l4816
 
9.1%
42408
 
4.5%
72408
 
4.5%
82408
 
4.5%
02408
 
4.5%
52408
 
4.5%
O2408
 
4.5%
v2408
 
4.5%
Other values (6)14448
27.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number36120
68.2%
Lowercase Letter14448
 
27.3%
Uppercase Letter2408
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
112040
33.3%
24816
 
13.3%
42408
 
6.7%
72408
 
6.7%
82408
 
6.7%
02408
 
6.7%
52408
 
6.7%
62408
 
6.7%
32408
 
6.7%
92408
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
l4816
33.3%
v2408
16.7%
e2408
16.7%
r2408
16.7%
a2408
16.7%
Uppercase Letter
ValueCountFrequency (%)
O2408
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common36120
68.2%
Latin16856
31.8%

Most frequent character per script

Common
ValueCountFrequency (%)
112040
33.3%
24816
 
13.3%
42408
 
6.7%
72408
 
6.7%
82408
 
6.7%
02408
 
6.7%
52408
 
6.7%
62408
 
6.7%
32408
 
6.7%
92408
 
6.7%
Latin
ValueCountFrequency (%)
l4816
28.6%
O2408
14.3%
v2408
14.3%
e2408
14.3%
r2408
14.3%
a2408
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII52976
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112040
22.7%
24816
 
9.1%
l4816
 
9.1%
42408
 
4.5%
72408
 
4.5%
82408
 
4.5%
02408
 
4.5%
52408
 
4.5%
O2408
 
4.5%
v2408
 
4.5%
Other values (6)14448
27.3%

Bene_Geo_Desc
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
National
3354 
Maryland
 
546
Alaska
 
546
Wisconsin
 
546
Connecticut
 
546
Other values (51)
25766 

Length

Max length20
Median length13
Mean length8.606312292
Min length4

Characters and Unicode

Total characters269412
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNational
2nd rowNational
3rd rowNational
4th rowNational
5th rowNational

Common Values

ValueCountFrequency (%)
National3354
 
10.7%
Maryland546
 
1.7%
Alaska546
 
1.7%
Wisconsin546
 
1.7%
Connecticut546
 
1.7%
Kentucky546
 
1.7%
Michigan546
 
1.7%
Missouri546
 
1.7%
Mississippi546
 
1.7%
Oregon546
 
1.7%
Other values (46)23036
73.6%

Length

2022-10-27T00:46:31.683827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
national3354
 
8.8%
new2184
 
5.8%
north1092
 
2.9%
virginia1092
 
2.9%
south1092
 
2.9%
dakota1092
 
2.9%
carolina1092
 
2.9%
florida546
 
1.4%
rhode546
 
1.4%
island546
 
1.4%
Other values (53)25298
66.7%

Most occurring characters

ValueCountFrequency (%)
a38454
14.3%
i26468
 
9.8%
n22542
 
8.4%
o21996
 
8.2%
s17056
 
6.3%
e15366
 
5.7%
t13806
 
5.1%
r12142
 
4.5%
l11570
 
4.3%
N7722
 
2.9%
Other values (36)82290
30.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter224848
83.5%
Uppercase Letter37934
 
14.1%
Space Separator6630
 
2.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a38454
17.1%
i26468
11.8%
n22542
10.0%
o21996
9.8%
s17056
7.6%
e15366
 
6.8%
t13806
 
6.1%
r12142
 
5.4%
l11570
 
5.1%
h7098
 
3.2%
Other values (14)38350
17.1%
Uppercase Letter
ValueCountFrequency (%)
N7722
20.4%
M4940
13.0%
C3276
8.6%
I2756
 
7.3%
D2210
 
5.8%
O2184
 
5.8%
W2184
 
5.8%
A2184
 
5.8%
V1664
 
4.4%
T1118
 
2.9%
Other values (11)7696
20.3%
Space Separator
ValueCountFrequency (%)
6630
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin262782
97.5%
Common6630
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
a38454
14.6%
i26468
 
10.1%
n22542
 
8.6%
o21996
 
8.4%
s17056
 
6.5%
e15366
 
5.8%
t13806
 
5.3%
r12142
 
4.6%
l11570
 
4.4%
N7722
 
2.9%
Other values (35)75660
28.8%
Common
ValueCountFrequency (%)
6630
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII269412
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a38454
14.3%
i26468
 
9.8%
n22542
 
8.4%
o21996
 
8.2%
s17056
 
6.3%
e15366
 
5.7%
t13806
 
5.1%
r12142
 
4.5%
l11570
 
4.3%
N7722
 
2.9%
Other values (36)82290
30.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
27664 
Medicare Only
 
1820
Medicare & Medicaid
 
1820

Length

Max length19
Median length3
Mean length4.511627907
Min length3

Characters and Unicode

Total characters141232
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All27664
88.4%
Medicare Only1820
 
5.8%
Medicare & Medicaid1820
 
5.8%

Length

2022-10-27T00:46:31.861082image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:32.013083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all27664
75.2%
medicare3640
 
9.9%
only1820
 
5.0%
1820
 
5.0%
medicaid1820
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l57148
40.5%
A27664
19.6%
e9100
 
6.4%
d7280
 
5.2%
i7280
 
5.2%
M5460
 
3.9%
c5460
 
3.9%
a5460
 
3.9%
5460
 
3.9%
r3640
 
2.6%
Other values (4)7280
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter99008
70.1%
Uppercase Letter34944
 
24.7%
Space Separator5460
 
3.9%
Other Punctuation1820
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l57148
57.7%
e9100
 
9.2%
d7280
 
7.4%
i7280
 
7.4%
c5460
 
5.5%
a5460
 
5.5%
r3640
 
3.7%
n1820
 
1.8%
y1820
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
A27664
79.2%
M5460
 
15.6%
O1820
 
5.2%
Space Separator
ValueCountFrequency (%)
5460
100.0%
Other Punctuation
ValueCountFrequency (%)
&1820
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin133952
94.8%
Common7280
 
5.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
l57148
42.7%
A27664
20.7%
e9100
 
6.8%
d7280
 
5.4%
i7280
 
5.4%
M5460
 
4.1%
c5460
 
4.1%
a5460
 
4.1%
r3640
 
2.7%
O1820
 
1.4%
Other values (2)3640
 
2.7%
Common
ValueCountFrequency (%)
5460
75.0%
&1820
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII141232
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l57148
40.5%
A27664
19.6%
e9100
 
6.4%
d7280
 
5.2%
i7280
 
5.2%
M5460
 
3.9%
c5460
 
3.9%
a5460
 
3.9%
5460
 
3.9%
r3640
 
2.6%
Other values (4)7280
 
5.2%

Bene_Race_Desc
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
21008 
American Indian/Alaska Native
 
1716
Black/African American
 
1716
Asian/Pacific Islander
 
1716
Non-Hispanic White
 
1716
Other values (2)
3432 

Length

Max length29
Median length3
Mean length8.15282392
Min length3

Characters and Unicode

Total characters255216
Distinct characters30
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All21008
67.1%
American Indian/Alaska Native1716
 
5.5%
Black/African American1716
 
5.5%
Asian/Pacific Islander1716
 
5.5%
Non-Hispanic White1716
 
5.5%
Hispanic1716
 
5.5%
Other/Unknown1716
 
5.5%

Length

2022-10-27T00:46:32.158512image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:32.341541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all21008
52.7%
american3432
 
8.6%
indian/alaska1716
 
4.3%
native1716
 
4.3%
black/african1716
 
4.3%
asian/pacific1716
 
4.3%
islander1716
 
4.3%
non-hispanic1716
 
4.3%
white1716
 
4.3%
hispanic1716
 
4.3%

Most occurring characters

ValueCountFrequency (%)
l47164
18.5%
A29588
11.6%
n22308
 
8.7%
i22308
 
8.7%
a22308
 
8.7%
c13728
 
5.4%
e10296
 
4.0%
s8580
 
3.4%
8580
 
3.4%
r8580
 
3.4%
Other values (20)61776
24.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter189592
74.3%
Uppercase Letter48464
 
19.0%
Space Separator8580
 
3.4%
Other Punctuation6864
 
2.7%
Dash Punctuation1716
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l47164
24.9%
n22308
11.8%
i22308
11.8%
a22308
11.8%
c13728
 
7.2%
e10296
 
5.4%
s8580
 
4.5%
r8580
 
4.5%
k5148
 
2.7%
t5148
 
2.7%
Other values (8)24024
12.7%
Uppercase Letter
ValueCountFrequency (%)
A29588
61.1%
H3432
 
7.1%
N3432
 
7.1%
I3432
 
7.1%
B1716
 
3.5%
P1716
 
3.5%
W1716
 
3.5%
O1716
 
3.5%
U1716
 
3.5%
Space Separator
ValueCountFrequency (%)
8580
100.0%
Other Punctuation
ValueCountFrequency (%)
/6864
100.0%
Dash Punctuation
ValueCountFrequency (%)
-1716
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin238056
93.3%
Common17160
 
6.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
l47164
19.8%
A29588
12.4%
n22308
9.4%
i22308
9.4%
a22308
9.4%
c13728
 
5.8%
e10296
 
4.3%
s8580
 
3.6%
r8580
 
3.6%
k5148
 
2.2%
Other values (17)48048
20.2%
Common
ValueCountFrequency (%)
8580
50.0%
/6864
40.0%
-1716
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII255216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l47164
18.5%
A29588
11.6%
n22308
 
8.7%
i22308
 
8.7%
a22308
 
8.7%
c13728
 
5.4%
e10296
 
4.0%
s8580
 
3.4%
8580
 
3.4%
r8580
 
3.4%
Other values (20)61776
24.2%

Bene_Sex_Desc
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
28184 
Male
 
1560
Female
 
1560

Length

Max length6
Median length3
Mean length3.199335548
Min length3

Characters and Unicode

Total characters100152
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All28184
90.0%
Male1560
 
5.0%
Female1560
 
5.0%

Length

2022-10-27T00:46:32.532779image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:32.681134image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all28184
90.0%
male1560
 
5.0%
female1560
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l59488
59.4%
A28184
28.1%
e4680
 
4.7%
a3120
 
3.1%
M1560
 
1.6%
F1560
 
1.6%
m1560
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter68848
68.7%
Uppercase Letter31304
31.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l59488
86.4%
e4680
 
6.8%
a3120
 
4.5%
m1560
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
A28184
90.0%
M1560
 
5.0%
F1560
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin100152
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l59488
59.4%
A28184
28.1%
e4680
 
4.7%
a3120
 
3.1%
M1560
 
1.6%
F1560
 
1.6%
m1560
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII100152
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l59488
59.4%
A28184
28.1%
e4680
 
4.7%
a3120
 
3.1%
M1560
 
1.6%
F1560
 
1.6%
m1560
 
1.6%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
26624 
Disabled
 
1560
Aged
 
1560
ESRD
 
1560

Length

Max length8
Median length3
Mean length3.348837209
Min length3

Characters and Unicode

Total characters104832
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th rowAll

Common Values

ValueCountFrequency (%)
All26624
85.0%
Disabled1560
 
5.0%
Aged1560
 
5.0%
ESRD1560
 
5.0%

Length

2022-10-27T00:46:32.820226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:32.980194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all26624
85.0%
disabled1560
 
5.0%
aged1560
 
5.0%
esrd1560
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l54808
52.3%
A28184
26.9%
D3120
 
3.0%
e3120
 
3.0%
d3120
 
3.0%
i1560
 
1.5%
s1560
 
1.5%
a1560
 
1.5%
b1560
 
1.5%
g1560
 
1.5%
Other values (3)4680
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter68848
65.7%
Uppercase Letter35984
34.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l54808
79.6%
e3120
 
4.5%
d3120
 
4.5%
i1560
 
2.3%
s1560
 
2.3%
a1560
 
2.3%
b1560
 
2.3%
g1560
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
A28184
78.3%
D3120
 
8.7%
E1560
 
4.3%
S1560
 
4.3%
R1560
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Latin104832
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l54808
52.3%
A28184
26.9%
D3120
 
3.0%
e3120
 
3.0%
d3120
 
3.0%
i1560
 
1.5%
s1560
 
1.5%
a1560
 
1.5%
b1560
 
1.5%
g1560
 
1.5%
Other values (3)4680
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII104832
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l54808
52.3%
A28184
26.9%
D3120
 
3.0%
e3120
 
3.0%
d3120
 
3.0%
i1560
 
1.5%
s1560
 
1.5%
a1560
 
1.5%
b1560
 
1.5%
g1560
 
1.5%
Other values (3)4680
 
4.5%

Bene_Age_Desc
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
25064 
0-64
 
1560
85 and over
 
1560
65-74
 
1560
75-84
 
1560

Length

Max length11
Median length3
Mean length3.647840532
Min length3

Characters and Unicode

Total characters114192
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowAll
3rd rowAll
4th rowAll
5th row0-64

Common Values

ValueCountFrequency (%)
All25064
80.1%
0-641560
 
5.0%
85 and over1560
 
5.0%
65-741560
 
5.0%
75-841560
 
5.0%

Length

2022-10-27T00:46:33.146097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:33.314238image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all25064
72.8%
0-641560
 
4.5%
851560
 
4.5%
and1560
 
4.5%
over1560
 
4.5%
65-741560
 
4.5%
75-841560
 
4.5%

Most occurring characters

ValueCountFrequency (%)
l50128
43.9%
A25064
21.9%
-4680
 
4.1%
54680
 
4.1%
44680
 
4.1%
83120
 
2.7%
3120
 
2.7%
63120
 
2.7%
73120
 
2.7%
01560
 
1.4%
Other values (7)10920
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter61048
53.5%
Uppercase Letter25064
21.9%
Decimal Number20280
 
17.8%
Dash Punctuation4680
 
4.1%
Space Separator3120
 
2.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l50128
82.1%
a1560
 
2.6%
n1560
 
2.6%
d1560
 
2.6%
o1560
 
2.6%
v1560
 
2.6%
e1560
 
2.6%
r1560
 
2.6%
Decimal Number
ValueCountFrequency (%)
54680
23.1%
44680
23.1%
83120
15.4%
63120
15.4%
73120
15.4%
01560
 
7.7%
Uppercase Letter
ValueCountFrequency (%)
A25064
100.0%
Dash Punctuation
ValueCountFrequency (%)
-4680
100.0%
Space Separator
ValueCountFrequency (%)
3120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin86112
75.4%
Common28080
 
24.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
l50128
58.2%
A25064
29.1%
a1560
 
1.8%
n1560
 
1.8%
d1560
 
1.8%
o1560
 
1.8%
v1560
 
1.8%
e1560
 
1.8%
r1560
 
1.8%
Common
ValueCountFrequency (%)
-4680
16.7%
54680
16.7%
44680
16.7%
83120
11.1%
3120
11.1%
63120
11.1%
73120
11.1%
01560
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII114192
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l50128
43.9%
A25064
21.9%
-4680
 
4.1%
54680
 
4.1%
44680
 
4.1%
83120
 
2.7%
3120
 
2.7%
63120
 
2.7%
73120
 
2.7%
01560
 
1.4%
Other values (7)10920
 
9.6%

Bene_RUCA_Desc
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size244.7 KiB
All
26624 
Urban
 
1560
Rural
 
1560
Unknown
 
1560

Length

Max length7
Median length3
Mean length3.398671096
Min length3

Characters and Unicode

Total characters106392
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowRural
3rd rowUrban
4th rowUnknown
5th rowAll

Common Values

ValueCountFrequency (%)
All26624
85.0%
Urban1560
 
5.0%
Rural1560
 
5.0%
Unknown1560
 
5.0%

Length

2022-10-27T00:46:33.486584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-27T00:46:33.661818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
all26624
85.0%
urban1560
 
5.0%
rural1560
 
5.0%
unknown1560
 
5.0%

Most occurring characters

ValueCountFrequency (%)
l54808
51.5%
A26624
25.0%
n6240
 
5.9%
U3120
 
2.9%
r3120
 
2.9%
a3120
 
2.9%
b1560
 
1.5%
R1560
 
1.5%
u1560
 
1.5%
k1560
 
1.5%
Other values (2)3120
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter75088
70.6%
Uppercase Letter31304
29.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l54808
73.0%
n6240
 
8.3%
r3120
 
4.2%
a3120
 
4.2%
b1560
 
2.1%
u1560
 
2.1%
k1560
 
2.1%
o1560
 
2.1%
w1560
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
A26624
85.0%
U3120
 
10.0%
R1560
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Latin106392
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
l54808
51.5%
A26624
25.0%
n6240
 
5.9%
U3120
 
2.9%
r3120
 
2.9%
a3120
 
2.9%
b1560
 
1.5%
R1560
 
1.5%
u1560
 
1.5%
k1560
 
1.5%
Other values (2)3120
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII106392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l54808
51.5%
A26624
25.0%
n6240
 
5.9%
U3120
 
2.9%
r3120
 
2.9%
a3120
 
2.9%
b1560
 
1.5%
R1560
 
1.5%
u1560
 
1.5%
k1560
 
1.5%
Other values (2)3120
 
2.9%

Total_Bene_Hosp
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct5691
Distinct (%)23.3%
Missing6919
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean3901.225384
Minimum0
Maximum1098471
Zeros1069
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:33.815170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q172
median312
Q31260
95-th percentile13091.6
Maximum1098471
Range1098471
Interquartile range (IQR)1188

Descriptive statistics

Standard deviation26147.52296
Coefficient of variation (CV)6.702387168
Kurtosis597.1735002
Mean3901.225384
Median Absolute Deviation (MAD)286
Skewness21.34513137
Sum95131381
Variance683692956.8
MonotonicityNot monotonic
2022-10-27T00:46:33.973208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01069
 
3.4%
14157
 
0.5%
11150
 
0.5%
15150
 
0.5%
12141
 
0.5%
17141
 
0.5%
13140
 
0.4%
20127
 
0.4%
22120
 
0.4%
16119
 
0.4%
Other values (5681)22071
70.5%
(Missing)6919
 
22.1%
ValueCountFrequency (%)
01069
3.4%
11150
 
0.5%
12141
 
0.5%
13140
 
0.4%
14157
 
0.5%
15150
 
0.5%
16119
 
0.4%
17141
 
0.5%
18117
 
0.4%
19101
 
0.3%
ValueCountFrequency (%)
10984711
< 0.1%
9838791
< 0.1%
9004061
< 0.1%
8665321
< 0.1%
8201601
< 0.1%
7925241
< 0.1%
7633761
< 0.1%
7328391
< 0.1%
6596701
< 0.1%
6586641
< 0.1%

Total_Mth_Enrl
Real number (ℝ≥0)

HIGH CORRELATION

Distinct29052
Distinct (%)92.9%
Missing25
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1198314.949
Minimum0
Maximum63977500
Zeros27
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:34.137990image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile885
Q126811.5
median166675
Q3603914.5
95-th percentile3724702.2
Maximum63977500
Range63977500
Interquartile range (IQR)577103

Descriptive statistics

Standard deviation5029060.724
Coefficient of variation (CV)4.19677709
Kurtosis70.72019862
Mean1198314.949
Median Absolute Deviation (MAD)160508
Skewness7.990315189
Sum3.748209328 × 1010
Variance2.529145176 × 1013
MonotonicityNot monotonic
2022-10-27T00:46:34.304568image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
027
 
0.1%
2623
 
0.1%
11714
 
< 0.1%
4214
 
< 0.1%
74012
 
< 0.1%
6012
 
< 0.1%
71310
 
< 0.1%
259
 
< 0.1%
419
 
< 0.1%
8749
 
< 0.1%
Other values (29042)31140
99.5%
(Missing)25
 
0.1%
ValueCountFrequency (%)
027
0.1%
244
 
< 0.1%
259
 
< 0.1%
2623
0.1%
278
 
< 0.1%
284
 
< 0.1%
301
 
< 0.1%
317
 
< 0.1%
324
 
< 0.1%
419
 
< 0.1%
ValueCountFrequency (%)
639775001
< 0.1%
639174651
< 0.1%
638673801
< 0.1%
638015131
< 0.1%
637189091
< 0.1%
635960921
< 0.1%
63519463.251
< 0.1%
634547211
< 0.1%
633433861
< 0.1%
632465611
< 0.1%

Total_Bene_Enr_Hosp_Per100K
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct23230
Distinct (%)95.3%
Missing6919
Missing (%)22.1%
Infinite0
Infinite (%)0.0%
Mean379.2055151
Minimum0
Maximum15402.2296
Zeros1069
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:34.481164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.39318
Q152.9269
median130.9715
Q3315.7946
95-th percentile1777.33082
Maximum15402.2296
Range15402.2296
Interquartile range (IQR)262.8677

Descriptive statistics

Standard deviation868.5077049
Coefficient of variation (CV)2.29033511
Kurtosis77.7447956
Mean379.2055151
Median Absolute Deviation (MAD)96.8195
Skewness7.302001883
Sum9246926.485
Variance754305.6335
MonotonicityNot monotonic
2022-10-27T00:46:34.810258image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01069
 
3.4%
118.10412
 
< 0.1%
35.69582
 
< 0.1%
123.68582
 
< 0.1%
173.06032
 
< 0.1%
149.41182
 
< 0.1%
260.18382
 
< 0.1%
145.80122
 
< 0.1%
65.40082
 
< 0.1%
87.95072
 
< 0.1%
Other values (23220)23298
74.4%
(Missing)6919
 
22.1%
ValueCountFrequency (%)
01069
3.4%
0.75451
 
< 0.1%
0.94981
 
< 0.1%
1.05341
 
< 0.1%
1.06791
 
< 0.1%
1.09771
 
< 0.1%
1.151
 
< 0.1%
1.1651
 
< 0.1%
1.20171
 
< 0.1%
1.23531
 
< 0.1%
ValueCountFrequency (%)
15402.22961
< 0.1%
14281.14811
< 0.1%
13973.23971
< 0.1%
13921.47471
< 0.1%
13535.28841
< 0.1%
13525.65821
< 0.1%
13345.72931
< 0.1%
13306.11031
< 0.1%
13022.82381
< 0.1%
12958.32911
< 0.1%

AVG_los
Real number (ℝ≥0)

MISSING

Distinct17892
Distinct (%)76.7%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean10.98673668
Minimum3.5
Maximum111.6923
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:35.015752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile7.9332
Q19.437
median10.4384
Q311.7312
95-th percentile15.32402
Maximum111.6923
Range108.1923
Interquartile range (IQR)2.2942

Descriptive statistics

Standard deviation3.383839798
Coefficient of variation (CV)0.3079931646
Kurtosis111.1984695
Mean10.98673668
Median Absolute Deviation (MAD)1.1137
Skewness7.31930628
Sum256177.7391
Variance11.45037178
MonotonicityNot monotonic
2022-10-27T00:46:35.182505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1050
 
0.2%
941
 
0.1%
1134
 
0.1%
1233
 
0.1%
825
 
0.1%
1319
 
0.1%
9.333318
 
0.1%
9.515
 
< 0.1%
715
 
< 0.1%
10.666715
 
< 0.1%
Other values (17882)23052
73.6%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
3.51
< 0.1%
3.66671
< 0.1%
4.27271
< 0.1%
4.38461
< 0.1%
4.51
< 0.1%
4.66671
< 0.1%
4.72221
< 0.1%
4.72732
< 0.1%
4.73911
< 0.1%
4.77271
< 0.1%
ValueCountFrequency (%)
111.69231
< 0.1%
83.21431
< 0.1%
77.281
< 0.1%
77.27271
< 0.1%
75.30771
< 0.1%
68.93751
< 0.1%
66.52171
< 0.1%
66.442
< 0.1%
65.07691
< 0.1%
64.251
< 0.1%

Pct_Dschrg_SNF
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct3237
Distinct (%)13.9%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.1616011751
Minimum0
Maximum0.7692
Zeros429
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:35.346898image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0551
Q10.1123
median0.1526
Q30.2
95-th percentile0.3043
Maximum0.7692
Range0.7692
Interquartile range (IQR)0.0877

Descriptive statistics

Standard deviation0.07647031276
Coefficient of variation (CV)0.47320394
Kurtosis1.832067219
Mean0.1616011751
Median Absolute Deviation (MAD)0.0432
Skewness0.8531320696
Sum3768.0546
Variance0.005847708734
MonotonicityNot monotonic
2022-10-27T00:46:35.506930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0429
 
1.4%
0.1667170
 
0.5%
0.1429156
 
0.5%
0.0909137
 
0.4%
0.125136
 
0.4%
0.2134
 
0.4%
0.1111121
 
0.4%
0.25119
 
0.4%
0.1818101
 
0.3%
0.153899
 
0.3%
Other values (3227)21715
69.4%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
0429
1.4%
0.00821
 
< 0.1%
0.01021
 
< 0.1%
0.0111
 
< 0.1%
0.01121
 
< 0.1%
0.01161
 
< 0.1%
0.01182
 
< 0.1%
0.01191
 
< 0.1%
0.0131
 
< 0.1%
0.0141
 
< 0.1%
ValueCountFrequency (%)
0.76921
< 0.1%
0.751
< 0.1%
0.61541
< 0.1%
0.61111
< 0.1%
0.58971
< 0.1%
0.58821
< 0.1%
0.58331
< 0.1%
0.56061
< 0.1%
0.55561
< 0.1%
0.55211
< 0.1%

Pct_Dschrg_Expired
Real number (ℝ≥0)

MISSING
ZEROS

Distinct2718
Distinct (%)11.7%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.1563636231
Minimum0
Maximum0.6667
Zeros411
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:35.681514image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0604
Q10.1192
median0.1538
Q30.1877
95-th percentile0.2667
Maximum0.6667
Range0.6667
Interquartile range (IQR)0.0685

Descriptive statistics

Standard deviation0.06341556097
Coefficient of variation (CV)0.4055646685
Kurtosis2.45021362
Mean0.1563636231
Median Absolute Deviation (MAD)0.0343
Skewness0.6643198982
Sum3645.9306
Variance0.004021533373
MonotonicityNot monotonic
2022-10-27T00:46:35.844859image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0411
 
1.3%
0.1667201
 
0.6%
0.2172
 
0.5%
0.1429162
 
0.5%
0.125161
 
0.5%
0.0909136
 
0.4%
0.1111123
 
0.4%
0.1818112
 
0.4%
0.25109
 
0.3%
0.1538103
 
0.3%
Other values (2708)21627
69.1%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
0411
1.3%
0.0071
 
< 0.1%
0.00781
 
< 0.1%
0.00881
 
< 0.1%
0.00981
 
< 0.1%
0.0111
 
< 0.1%
0.01121
 
< 0.1%
0.01151
 
< 0.1%
0.0121
 
< 0.1%
0.01252
 
< 0.1%
ValueCountFrequency (%)
0.66671
 
< 0.1%
0.54551
 
< 0.1%
0.53852
 
< 0.1%
0.5251
 
< 0.1%
0.52471
 
< 0.1%
0.52381
 
< 0.1%
0.56
< 0.1%
0.49231
 
< 0.1%
0.4752
 
< 0.1%
0.47372
 
< 0.1%

Pct_Dschrg_Home
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct4590
Distinct (%)19.7%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.408410992
Minimum0
Maximum1
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:36.019189image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2115
Q10.3411
median0.4064
Q30.4793
95-th percentile0.5972
Maximum1
Range1
Interquartile range (IQR)0.1382

Descriptive statistics

Standard deviation0.1138527416
Coefficient of variation (CV)0.2787700229
Kurtosis0.4820147389
Mean0.408410992
Median Absolute Deviation (MAD)0.069
Skewness0.02031304681
Sum9522.9191
Variance0.01296244677
MonotonicityNot monotonic
2022-10-27T00:46:36.191590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5299
 
1.0%
0.3333130
 
0.4%
0.4130
 
0.4%
0.428698
 
0.3%
0.2588
 
0.3%
0.444476
 
0.2%
0.676
 
0.2%
0.416770
 
0.2%
0.37567
 
0.2%
0.363667
 
0.2%
Other values (4580)22216
71.0%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
011
< 0.1%
0.01331
 
< 0.1%
0.01821
 
< 0.1%
0.02561
 
< 0.1%
0.0271
 
< 0.1%
0.02841
 
< 0.1%
0.03031
 
< 0.1%
0.03331
 
< 0.1%
0.03851
 
< 0.1%
0.0391
 
< 0.1%
ValueCountFrequency (%)
12
< 0.1%
0.94441
 
< 0.1%
0.90912
< 0.1%
0.8751
 
< 0.1%
0.86672
< 0.1%
0.85713
< 0.1%
0.83334
< 0.1%
0.82351
 
< 0.1%
0.82141
 
< 0.1%
0.81824
< 0.1%

Pct_Dschrg_Hspc
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1366
Distinct (%)5.9%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.03785352318
Minimum0
Maximum0.3077
Zeros2647
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:36.364471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0185
median0.0345
Q30.05
95-th percentile0.0922
Maximum0.3077
Range0.3077
Interquartile range (IQR)0.0315

Descriptive statistics

Standard deviation0.029358534
Coefficient of variation (CV)0.775582602
Kurtosis5.49512426
Mean0.03785352318
Median Absolute Deviation (MAD)0.0156
Skewness1.63030703
Sum882.6306
Variance0.0008619235187
MonotonicityNot monotonic
2022-10-27T00:46:36.523365image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02647
 
8.5%
0.0455111
 
0.4%
0.04109
 
0.3%
0.037109
 
0.3%
0.043596
 
0.3%
0.033395
 
0.3%
0.038594
 
0.3%
0.0593
 
0.3%
0.047692
 
0.3%
0.035791
 
0.3%
Other values (1356)19780
63.2%
(Missing)7987
25.5%
ValueCountFrequency (%)
02647
8.5%
0.00121
 
< 0.1%
0.00191
 
< 0.1%
0.00211
 
< 0.1%
0.00221
 
< 0.1%
0.00233
 
< 0.1%
0.00242
 
< 0.1%
0.00251
 
< 0.1%
0.00276
 
< 0.1%
0.00282
 
< 0.1%
ValueCountFrequency (%)
0.30771
 
< 0.1%
0.28572
< 0.1%
0.27274
< 0.1%
0.26322
< 0.1%
0.251
 
< 0.1%
0.24241
 
< 0.1%
0.23813
< 0.1%
0.23531
 
< 0.1%
0.23441
 
< 0.1%
0.23083
< 0.1%

Pct_Dschrg_HomeHealth
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct2615
Distinct (%)11.2%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.1615338809
Minimum0
Maximum0.5833
Zeros295
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:36.694928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0667
Q10.1233
median0.1589
Q30.1969
95-th percentile0.2638
Maximum0.5833
Range0.5833
Interquartile range (IQR)0.0736

Descriptive statistics

Standard deviation0.06165337656
Coefficient of variation (CV)0.3816745827
Kurtosis1.63453822
Mean0.1615338809
Median Absolute Deviation (MAD)0.0368
Skewness0.4442146183
Sum3766.4855
Variance0.003801138842
MonotonicityNot monotonic
2022-10-27T00:46:36.864059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0295
 
0.9%
0.1429185
 
0.6%
0.1667184
 
0.6%
0.2166
 
0.5%
0.25146
 
0.5%
0.125145
 
0.5%
0.1111120
 
0.4%
0.1818117
 
0.4%
0.1538114
 
0.4%
0.1333101
 
0.3%
Other values (2605)21744
69.5%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
0295
0.9%
0.01271
 
< 0.1%
0.01371
 
< 0.1%
0.01381
 
< 0.1%
0.01491
 
< 0.1%
0.01541
 
< 0.1%
0.01611
 
< 0.1%
0.01642
 
< 0.1%
0.01721
 
< 0.1%
0.01791
 
< 0.1%
ValueCountFrequency (%)
0.58331
 
< 0.1%
0.57141
 
< 0.1%
0.521
 
< 0.1%
0.55
< 0.1%
0.47621
 
< 0.1%
0.47373
< 0.1%
0.46921
 
< 0.1%
0.46155
< 0.1%
0.45781
 
< 0.1%
0.45455
< 0.1%

Pct_Dschrg_Other
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct1636
Distinct (%)7.0%
Missing7987
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean0.07219999142
Minimum0
Maximum0.4615
Zeros1028
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size244.7 KiB
2022-10-27T00:46:37.039917image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01558
Q10.0511
median0.068
Q30.0887
95-th percentile0.1366
Maximum0.4615
Range0.4615
Interquartile range (IQR)0.0376

Descriptive statistics

Standard deviation0.03736036706
Coefficient of variation (CV)0.517456669
Kurtosis5.46073485
Mean0.07219999142
Median Absolute Deviation (MAD)0.0186
Skewness1.264919624
Sum1683.4872
Variance0.001395797027
MonotonicityNot monotonic
2022-10-27T00:46:37.198199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01028
 
3.3%
0.0769175
 
0.6%
0.0833169
 
0.5%
0.0714168
 
0.5%
0.0667166
 
0.5%
0.0909161
 
0.5%
0.0588151
 
0.5%
0.0625148
 
0.5%
0.1111113
 
0.4%
0.0556110
 
0.4%
Other values (1626)20928
66.9%
(Missing)7987
 
25.5%
ValueCountFrequency (%)
01028
3.3%
0.00381
 
< 0.1%
0.00441
 
< 0.1%
0.00471
 
< 0.1%
0.00562
 
< 0.1%
0.00652
 
< 0.1%
0.0071
 
< 0.1%
0.00711
 
< 0.1%
0.00721
 
< 0.1%
0.00731
 
< 0.1%
ValueCountFrequency (%)
0.46151
 
< 0.1%
0.42861
 
< 0.1%
0.42111
 
< 0.1%
0.36841
 
< 0.1%
0.36362
 
< 0.1%
0.361
 
< 0.1%
0.35711
 
< 0.1%
0.351
 
< 0.1%
0.33335
< 0.1%
0.31821
 
< 0.1%

Interactions

2022-10-27T00:46:27.397935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:08.008718image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:10.233671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:12.147441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:14.163220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:16.000083image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:19.487980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.467644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:23.305616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.426101image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:27.581962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:08.329914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:10.418776image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:12.346376image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:14.345183image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:16.196971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:19.695098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.639027image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:23.482047image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.612346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:27.763328image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:08.599475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:10.607914image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:12.565158image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:14.524656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:16.419385image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:19.882236image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.809650image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:23.674060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.792232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:27.967825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:08.870664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:10.812878image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:12.767956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:14.708657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:16.625797image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:20.083689image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.996780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:23.879094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.986845image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:28.128019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:09.069595image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.015763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:12.967239image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:14.873229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:16.832633image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:20.287208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:22.165470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:24.059479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:26.166462image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:28.349640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:09.297652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.212861image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:13.180434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:15.092190image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:17.043059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:20.497148image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:22.368987image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:24.281995image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:26.361566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:28.538343image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:09.494545image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.409135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:13.385584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:15.290156image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:18.655895image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:20.720240image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:22.578459image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:24.525166image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:26.690170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:28.708162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:09.676828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.592273image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:13.574506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:15.470658image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:18.873787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:20.899901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:22.765389image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:24.759413image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:26.871110image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:28.887624image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:09.866971image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.780783image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:13.774427image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:15.645119image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:19.089442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.082170image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:22.954587image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.008541image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:27.050041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:29.076301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:10.058937image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:11.977613image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:13.972706image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:15.839144image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:19.284122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:21.294896image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:23.126155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:25.247963image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-10-27T00:46:27.229100image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-10-27T00:46:37.353234image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-27T00:46:37.650725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-27T00:46:37.940475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-27T00:46:38.224429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-27T00:46:38.479947image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-27T00:46:29.395586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-27T00:46:29.941179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-27T00:46:30.434041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-27T00:46:30.869358image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

YearMonthBene_Geo_DescBene_Mdcd_Mdcr_Enrl_StusBene_Race_DescBene_Sex_DescBene_Mdcr_Entlmt_StusBene_Age_DescBene_RUCA_DescTotal_Bene_HospTotal_Mth_EnrlTotal_Bene_Enr_Hosp_Per100KAVG_losPct_Dschrg_SNFPct_Dschrg_ExpiredPct_Dschrg_HomePct_Dschrg_HspcPct_Dschrg_HomeHealthPct_Dschrg_Other
02020OverallNationalAllAllAllAllAllAll983879.06.251189e+071573.907010.57870.19260.17990.35710.04930.15320.0679
12020OverallNationalAllAllAllAllAllRural189003.01.206914e+071566.00289.70350.17410.17550.39410.03910.14430.0728
22020OverallNationalAllAllAllAllAllUrban792524.04.981956e+071590.788810.78100.19700.18090.34830.05170.15550.0667
32020OverallNationalAllAllAllAllAllUnknownNaN6.231943e+05NaNNaNNaNNaNNaNNaNNaNNaN
42020OverallNationalAllAllAllAll0-64All130239.08.319817e+061565.407011.58410.14750.11950.49550.01450.13050.0926
52020OverallNationalAllAllAllAll65-74All341344.03.103836e+071099.748811.13540.14540.15850.44630.02440.15060.0748
62020OverallNationalAllAllAllAll75-84All317125.01.651179e+071920.596910.42000.20520.19670.30910.05250.17260.0639
72020OverallNationalAllAllAllAll85 and overAll195063.06.641918e+062936.84749.17700.28490.23040.18680.11090.14130.0458
82020OverallNationalAllAllAllAgedAllAll820160.05.389008e+071521.912810.34820.19690.18840.33900.05530.15670.0637
92020OverallNationalAllAllAllDisabledAllAll106881.08.062489e+061325.657611.54140.14740.11420.50040.01550.12880.0937

Last rows

YearMonthBene_Geo_DescBene_Mdcd_Mdcr_Enrl_StusBene_Race_DescBene_Sex_DescBene_Mdcr_Entlmt_StusBene_Age_DescBene_RUCA_DescTotal_Bene_HospTotal_Mth_EnrlTotal_Bene_Enr_Hosp_Per100KAVG_losPct_Dschrg_SNFPct_Dschrg_ExpiredPct_Dschrg_HomePct_Dschrg_HspcPct_Dschrg_HomeHealthPct_Dschrg_Other
31294202112South DakotaAllAllAllAllAllUnknownNaN30.0NaNNaNNaNNaNNaNNaNNaNNaN
31295202112TennesseeAllAllAllAllAllUnknownNaN2590.0NaNNaNNaNNaNNaNNaNNaNNaN
31296202112TexasAllAllAllAllAllUnknownNaN6237.0NaNNaNNaNNaNNaNNaNNaNNaN
31297202112UtahAllAllAllAllAllUnknownNaN117.0NaNNaNNaNNaNNaNNaNNaNNaN
31298202112VermontAllAllAllAllAllUnknownNaN457.0NaNNaNNaNNaNNaNNaNNaNNaN
31299202112VirginiaAllAllAllAllAllUnknownNaN2666.0NaNNaNNaNNaNNaNNaNNaNNaN
31300202112WashingtonAllAllAllAllAllUnknownNaN885.0NaNNaNNaNNaNNaNNaNNaNNaN
31301202112West VirginiaAllAllAllAllAllUnknownNaN928.0NaNNaNNaNNaNNaNNaNNaNNaN
31302202112WisconsinAllAllAllAllAllUnknownNaN217.0NaNNaNNaNNaNNaNNaNNaNNaN
31303202112WyomingAllAllAllAllAllUnknownNaN1356.0NaNNaNNaNNaNNaNNaNNaNNaN